Deep Convolution Neural Networks (Deep CNNs) are at the heart of the remarkable development in deep learning. CNNs have already been used in the 90's to solve problems of character recognition, but their use has become as widespread as it is now thanks to recent researches. These CNNs won the 2012 ImageNet image classification tournament, crushing other competitors. Then, the convolutional neural network became a very useful tool in the field of the machine learning.
As such, the CNNs can be used for surveillance systems of protected facilities like banks, military bases, etc. That is, the CNNs processes CCTV video data for detection of rare events in real-time, for example, robbers breaking into the banks or breach of perimeter by an enemy. It will be faster and more efficient to operate and manage the surveillance systems than conventional human monitoring of the CCTV video data.
However, implementation of the surveillance systems as such is difficult because training images to be used for learning the CNNs are scarce. The learning of the CNNs requires more than tens of thousands of the training images, but the training images related to the rare events are very rare by definition. As a result, the learning of the CNNs is difficult and the surveillance systems as such are still impractical.